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Tooth Cutting Machining of Cycloid Coupling based on Monolithic Cutter
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Development and interpretation of machine learning-based prognostic models for predicting high-risk prognostic pathological components in pulmonary nodules: integrating clinical fe...
Published 2025-06-01“…In this study, we aimed to build a multi-parameter machine learning predictive model to improve the discrimination accuracy of HRPPC. …”
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渐开线锥形齿轮的数控连续展成磨削
Published 2001-01-01“…This paper deals with computing principles of worm grinding wheel parameters, machine setting parameters and internal driving parameters of the machine and machining method when grinding conical involute gear on CNC worm gear grinding machine.…”
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Machine Learning-Based Model in Predicting the Plate-End Debonding of FRP-Strengthened RC Beams in Flexure
Published 2022-01-01“…In this study, considering the extremely complicated nonlinear relationship between the PE debonding and the parameters, machine learning algorithms, namely, linear regression, ridge regression, decision tree, random forest, and neural network improved by sparrow search algorithm, are established to predict the PE debonding of RC beams strengthened with FRP. …”
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Effect of plateletpheresis on donor variables and platelet yield using three different cell separators: Experience from tertiary care hospital
Published 2024-12-01“…Donor variables, pre, and post-plateletpheresis hematological parameters, machine variables, platelet yield were observed. …”
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Online learning to accelerate nonlinear PDE solvers: Applied to multiphase porous media flow
Published 2025-12-01“…Furthermore, this work performs a sensitivity study in the dimensionless parameters (machine learning features), assess the efficacy of various machine learning models, demonstrate a decrease in nonlinear iterations using our method in more intricate, realistic three-dimensional models, and fully couple a machine learning model into an open-source multiphase flow simulator achieving up to 85% reduction in computational time.…”
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New insight into viscosity prediction of imidazolium-based ionic liquids and their mixtures with machine learning models
Published 2025-07-01“…This study aims to predict the viscosity of imidazolium-based ILs and their mixtures using critical properties as input parameters. Machine learning (ML) models have been implemented, and their performance in viscosity prediction for IL mixtures was compared with a molecular-based model, ePC-SAFT-FVT (ePC-FVT-MB), and an ion-based model, ePC-SAFT-FVT (ePC-FVT-MB). …”
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Optimization of aluminum 6061 surface integrity on dry-running machining CNC milling using Taguchi methods
Published 2023-09-01“…Aluminum 6061 is chosen as the material. The observed parameter machining in this research is cutting speed 3 is levels 60, 70, 80 mm/minute, then depth of cut is 3 levels 300, 380, 450 mm/minute and depth of cut is 3 levels 0.5, 0.75, 1.0mm. …”
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Formation of Machine Learning Features Based on the Construction of Tropical Functions
Published 2022-09-01“…To increase the variety of parameters (machine learning features), filtering of object scanning by rows from left to right and scanning by columns from bottom to top are built. …”
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Development of predictive models for differential diagnosis of hypertrophic cardiomyopathy
Published 2024-12-01“…The original dataset contains 74 parameters. Machine learning models of the following classes were created and optimized: logistic regression, support vector machine, decision tree, and gradient boosting decision trees.Results. …”
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Flax Harvesting Technologies for Flax Harvesting Machines
Published 2023-04-01“…(Research purpose) To establish patterns and the degree of correlation between the qualitative operation indicators (pulling and deseeding quality, flax line stretching); design parameters; machine dynamic properties and harvesting conditions (height and density of flax stem, field surface, thickness and unevenness of flax straw, etc.). …”
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Predicting the compressive strength of polymer-infused bricks: A machine learning approach with SHAP interpretability
Published 2025-03-01“…The polymer bricks’ compressive strength was recorded as the output parameter, with cement, fly ash, M sand, PP waste, and age serving as the input parameters. Machine learning models often function as black boxes, thereby providing limited interpretability; however, our approach addresses this limitation by employing the SHapley Additive exPlanations (SHAP) interpretation method. …”
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